Re-emerging Infectious Disease (RED) Alert tool

Definitions of “re-emerging infectious diseases” typically encompass any disease occurrence that was a historic public health threat, declined dramatically, and has since presented itself again as a significant health problem. Examples include antimicrobial resistance leading to resurgence of tuberculosis, or measles re-appearing in previously protected communities.

January 25, 2018

Spatial temporal cluster analysis to enhance awareness of disease re-emergence on a global scale

The re-emergence of an infectious disease is dependent on social, political, behavioral, and disease-specific factors. Global disease surveillance is a requisite of early detection that facilitates coordinated interventions to these events. Novel informatics tools developed from publicly available data are constantly evolving with the incorporation of new data streams. Re-emerging Infectious Disease (RED) Alert is an open-source tool designed to help analysts develop a contextual framework when planning for future events, given what has occurred in the past.

January 25, 2018

Analytics for Investigation of Disease Outbreaks (AIDO)

Situational awareness, or the understanding of elemental components of an event with respect to both time and space, is critical for public health decision-makers during an infectious disease outbreak. AIDO is a web-based tool designed to contextualize incoming infectious disease information during an unfolding event for decision-making purposes.

Objective:

January 25, 2018

Facilitating the Use of Epidemiological Models for Infectious Disease Surveillance

Epidemiological modeling for infectious disease is useful for disease management and routine implementation needs to be facilitated through better description of models in an operational context. A standardized model characterization process that allows selection or making manual comparisons of available models and their results is currently lacking. Los Alamos National Laboratory (LANL) has developed a comprehensive framework that can be used to characterize an infectious disease model in an operational context.

June 19, 2017

Global Disease Monitoring and Forecasting with Wikipedia

Infectious disease remains costly in human and economic terms. Effective and timely disease surveillance is a critical component of prevention and mitigation strategies. The limitations of traditional disease surveillance systems have motivated new techniques based upon internet data sources such as search queries and social media. However, 4 challenges remain before internet-based disease surveillance models can be reliably integrated into an operational system: openness, breadth, transferability, and forecasting.

August 31, 2017

Evaluation of Point of Need Diagnostic Tests for Use in California Influenza Outbreaks

Each year several thousands contract the seasonal flu, and it is estimated that these viruses are responsible for the deaths of over six thousand individuals [1]. Further, when a new strain is detected (e.g. 2009), the result can be substantially more dramatic [2]. Because of the potential threats flu viruses pose, the United States, like many developed countries, has a very well established flu surveillance system consisting of 10 components collecting laboratory data, mortality data, hospitalization data and sentinel outpatient care data [3].

August 31, 2017

Situational Awareness for Unfolding Gastrointestinal Outbreaks Using Historical Data

The CDC defines a foodborne outbreak as two or more people getting the same illness from the same contaminated food or drink. These illnesses are often characterized as gastroenteritis until the causative agent is identified (bacterial or viral). Due to the globally interconnected food distribution system, local foodborne disease outbreaks often have global impacts. Therefore, the rapid detection of a gastroenteritis outbreak is of utmost importance for effective control.

September 08, 2017

Military and Civilian Disease Outbreaks: A Comparative Analysis

Using influenza like illness (ILI) data from the repository held by AFHSC, and publically available malaria data we characterized similarities and differences between military and civilian outbreaks. Pete Riley et al. utilized a similar ILI dataset to investigate civilian and military outbreaks similarity during the 2009 flu epidemic. They found, overall, high similarity between civilian and military outbreaks, with military peaking roughly one week after civilian.

September 11, 2017

Tools and Apps to Enhance Situational Awareness for Global Disease Surveillance

Situational awareness is important for early warning and early detection of infectious disease outbreaks and occurs at both local and global scales. Los Alamos National Laboratory (LANL) is developing a suite of tools to provide actionable information and knowledge for enhanced situational awareness during an unfolding event.

July 06, 2017

Contextualizing Data Streams for Infectious Disease Surveillance

Los Alamos National Laboratory (LANL) was tasked with developing methods to determine the relevance of data streams for an integrated global biosurveillance system. We used a novel method of evaluating the effectiveness of data streams called the 'surveillance window'. We defined a surveillance window as the brief period of time when information gathered can be used to assist decision makers in effectively responding to an impending outbreak. Information obtained for data streams beyond this window is deemed to have limited use.

Objective

August 22, 2018

Pages

Contact Us

National Syndromic
Surveillance Program

Email:nssp@cdc.gov

The National Syndromic Surveillance Program (NSSP) is a collaboration among states and public health jurisdictions that contribute data to the BioSense Platform, public health practitioners who use local syndromic surveillance systems, Center for Disease Control and Prevention programs, other federal agencies, partner organizations, hospitals, healthcare professionals, and academic institutions.

Site created by Fusani Applications